Advertising cannibalization management

ABSTRACT

A system and method for advertising cannibalization management are provided. In example embodiments, historical data comprising advertisement revenue, advertisement parameters, and a cannibalization metric are accessed. The cannibalization metric is indicative of sales loss associated with an advertisement presentation. A value for at least one of the advertisement parameters that, when used, causes a desired advertisement revenue with respect to a bounded cannibalization metric is determined by analyzing the historical data. An advertisement is presented, in real time, on a user interface of a client device using the determined value.

RELATED APPLICATIONS

This application claims the priority benefit of U.S. ProvisionalApplication No. 61/913,157, entitled “SYSTEM TO OPTIMIZE ADVERTISING FORCANNIBALIZATION MANAGEMENT,” filed Dec. 6, 2013, which is herebyincorporated by reference in its entirety.

TECHNICAL FIELD

Embodiments of the present disclosure relate generally to computertechnology and, more particularly, but not by way of limitation, toadvertising cannibalization management.

BACKGROUND

Digital advertising has become a significant source of revenue for manyonline companies. However, excessive advertising can cause a loss insales revenue for websites that sell products to consumers. Forinstance, a sales cannibalization occurs when an advertisement directs aconsumer away from a website selling products and potential sales to theconsumer at the website are lost.

BRIEF DESCRIPTION OF THE DRAWINGS

Various ones of the appended drawings merely illustrate exampleembodiments of the present disclosure and should not be considered aslimiting its scope.

FIG. 1 is a block diagram illustrating a networked system, according tosome example embodiments.

FIG. 2 is a block diagram showing example components provided within thesystem of FIG. 1, according to some example embodiments.

FIG. 3 is a block diagram illustrating an example embodiment of acannibalization regulation system, according to some exampleembodiments.

FIG. 4 is a block diagram illustrating an example method for advertisingcannibalization management, according to some example embodiments.

FIG. 5 is a flow diagram illustrating an example method for advertisingcannibalization management, according to some example embodiments.

FIG. 6 is a flow diagram illustrating example operations for determiningan advertisement parameter, according to some example embodiments.

FIG. 7 is a flow diagram illustrating example operations for identifyinga cannibalization covariate, according to some example embodiments.

FIGS. 8 and 9 are user interface diagrams depicting various example userinterfaces, according to some example embodiments.

FIG. 10 is a block diagram illustrating an example of a softwarearchitecture that may be installed on a machine, according to someexample embodiments.

FIG. 11 is a block diagram presenting a diagrammatic representation of amachine in the form of a computer system within which a set ofinstructions may be executed for causing the machine to perform any oneor more of the methodologies discussed herein, according to an exampleembodiment.

The headings provided herein are merely for convenience and do notnecessarily affect the scope or meaning of the terms used.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments of the inventive subject matter. It will be evident,however, to those skilled in the art, that embodiments of the inventivesubject matter may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

A website that sells products to consumers can also generate revenuethough third party advertisements (also referred to as “ads”). In somecases, sales revenue is more beneficial than advertising revenue asthere is a high value per transaction, and each sale can increasecustomer loyalty, which may lead to future sales revenue throughrepeated business. However, frequency of sales transactions can berelatively small or sporadic. Advertisement revenue is a secondarysource of revenue to monetize non-converting user sessions (e.g., asession where a customer did not make a purchase). In some instances,advertising causes sales cannibalizations. A sales cannibalizationoccurs, for example, when a user is visiting the website, sees anadvertisement, clicks on the advertisement, and is directed away fromthe website. In this example, a potential sale to the user is lost whenthe user is directed away from the website. Removing all advertisementsfrom the website can stop sales cannibalization caused byadvertisements, but may not maximize total revenue.

In various example embodiments, a cannibalization regulation system isemployed to manage sales cannibalization while improving or enhancingadvertisement revenue. The cannibalization regulation system accesseshistorical data including advertisement revenue, advertisementparameters (e.g., advertisement placement, impressions, clicks), and acannibalization metric. The cannibalization metric is indicative ofsales loss associated with an advertisement presentation. Thecannibalization regulation system determines a value for one or moreadvertisement parameters that causes a desired advertisement revenue ormaximizes/optimizes the advertisement revenue with respect to a boundedcannibalization metric by analyzing the historical data. Subsequently,the cannibalization regulation system causes presentation of anadvertisement on a user interface of a client device using thedetermined value. In this way, the advertisement revenue can bespecified, improved, enhanced, or optimized while maintaining a certainlevel of sales cannibalization.

With reference to FIG. 1, an example embodiment of a high-levelclient-server-based network architecture 100 is shown. A networkedsystem 102 provides server-side functionality via a network 104 (e.g.,the Internet or wide area network (WAN)) to a client device 110. In someimplementations, a user (e.g., user 106) interacts with the networkedsystem 102 using the client device 110. FIG. 1 illustrates, for example,a web client 112 (e.g., a browser, such as the INTERNET EXPLORER®browser developed by MICROSOFT® Corporation of Redmond, Wash. State),client application(s) 114, and a programmatic client 116 executing onthe client device 110. The client device 110 includes the web client112, the client application(s) 114, and the programmatic client 116alone, together, or in any suitable combination. Although FIG. 1 showsone client device 110, in other implementations, the networkarchitecture 100 comprises multiple client devices.

In various implementations, the client device 110 comprises a computingdevice that includes at least a display and communication capabilitiesthat provide access to the networked system 102 via the network 104. Theclient device 110 comprises, but is not limited to, a remote device,work station, computer, general purpose computer, Internet appliance,hand-held device, wireless device, portable device, wearable computer,cellular or mobile phone, Personal Digital Assistant (PDA), smart phone,tablet, ultrabook, netbook, laptop, desktop, multi-processor system,microprocessor-based or programmable consumer electronic, game consoles,set-top box, network Personal Computer (PC), mini-computer, and soforth. In an example embodiment, the client device 110 comprises one ormore of a touch screen, accelerometer, gyroscope, biometric sensor,camera, microphone, Global Positioning System (GPS) device, and thelike.

The client device 110 communicates with the network 104 via a wired orwireless connection. For example, one or more portions of the network104 comprises an ad hoc network, an intranet, an extranet, a VirtualPrivate Network (VPN), a Local Area Network (LAN), a wireless LAN(WLAN), a Wide Area Network (WAN), a wireless WAN (WWAN), a MetropolitanArea Network (MAN), a portion of the Internet, a portion of the PublicSwitched Telephone Network (PSTN), a cellular telephone network, awireless network, a Wireless Fidelity (WI-FE)) network, a WorldwideInteroperability for Microwave Access (WiMax) network, another type ofnetwork, or any suitable combination thereof.

In some example embodiments, the client device 110 includes one or moreof the applications (also referred to as “apps”) such as, but notlimited to, web browsers, book reader apps (operable to read e-books),media apps (operable to present various media forms including audio andvideo), fitness apps, biometric monitoring apps, messaging apps,electronic mail (email) apps, and e-commerce site apps (also referred toas “marketplace apps”). In some implementations, the clientapplication(s) 114 include various components operable to presentinformation to the user and communicate with networked system 102. Insome embodiments, if the e-commerce site application is included in theclient device 110, then this application is configured to locallyprovide the user interface and at least some of the functionalities withthe application configured to communicate with the networked system 102,on an as needed basis, for data or processing capabilities not locallyavailable (e.g., access to a database of items available for sale, toauthenticate a user, to verify a method of payment). Conversely, if thee-commerce site application is not included in the client device 110,the client device 110 can use its web browser to access the e-commercesite (or a variant thereof) hosted on the networked system 102.

The web client 112 accesses the various systems of the networked system102 via the web interface supported by a web server 122. Similarly, theprogrammatic client 116 and client application(s) 114 accesses thevarious services and functions provided by the networked system 102 viathe programmatic interface provided by an Application Program Interface(API) server 120. The programmatic client 116 can, for example, be aseller application (e.g., the Turbo Lister application developed byEBAY® Inc., of San Jose, Calif.) to enable sellers to author and managelistings on the networked system 102 in an off-line manner, and toperform batch-mode communications between the programmatic client 116and the networked system 102.

Users (e.g., the user 106) comprise a person, a machine, or other meansof interacting with the client device 110. In some example embodiments,the user is not part of the network architecture 100, but interacts withthe network architecture 100 via the client device 110 or another means.For instance, the user provides input (e.g., touch screen input oralphanumeric input) to the client device 110 and the input iscommunicated to the networked system 102 via the network 104. In thisinstance, the networked system 102, in response to receiving the inputfrom the user, communicates information to the client device 110 via thenetwork 104 to be presented to the user. In this way, the user caninteract with the networked system 102 using the client device 110.

The API server 120 and the web server 122 are coupled to, and provideprogrammatic and web interfaces respectively to, one or more applicationserver(s) 140. The application server(s) 140 can host one or morepublication system(s) 142, payment system(s) 144, and a cannibalizationregulation system 150, each of which comprises one or more modules orapplications and each of which can be embodied as hardware, software,firmware, or any combination thereof. The application server(s) 140 are,in turn, shown to be coupled to one or more database server(s) 124 thatfacilitate access to one or more information storage repositories ordatabase(s) 126. In an example embodiment, the database(s) 126 arestorage devices that store information to be posted (e.g., publicationsor listings) to the publication system(s) 142. The database(s) 126 alsostores digital good information in accordance with some exampleembodiments.

Additionally, a third party application 132, executing on third partyserver(s) 130, is shown as having programmatic access to the networkedsystem 102 via the programmatic interface provided by the API server120. For example, the third party application 132, utilizing informationretrieved from the networked system 102, supports one or more featuresor functions on a website hosted by the third party. The third partywebsite, for example, provides one or more promotional, marketplace, orpayment functions that are supported by the relevant applications of thenetworked system 102.

The publication system(s) 142 provides a number of publication functionsand services to the users that access the networked system 102. Thepayment system(s) 144 likewise provides a number of functions to performor facilitate payments and transactions. While the publication system(s)142 and payment system(s) 144 are shown in FIG. 1 to both form part ofthe networked system 102, it will be appreciated that, in alternativeembodiments, each system 142 and 144 may form part of a payment servicethat is separate and distinct from the networked system 102. In someexample embodiments, the payment system(s) 144 may form part of thepublication system(s) 142.

In some implementations, the cannibalization regulation system 150provides functionality to improve advertisement revenue whilemaintaining a specified or dynamically determined level ofcannibalization. In some example embodiments, the cannibalizationregulation system 150 communicates with the client device 110, the thirdparty server(s) 130, the publication system(s) 142 (e.g., retrievinglistings), and the payment system(s) 144 (e.g., purchasing a listing).In an alternative example embodiment, the cannibalization regulationsystem 150 is a part of the publication system(s) 142. Thecannibalization regulation system 150 will be discussed further inconnection with FIG. 3 below.

Further, while the client-server-based network architecture 100 shown inFIG. 1 employs a client-server architecture, the present inventivesubject matter is, of course, not limited to such an architecture, andcan equally well find application in a distributed, or peer-to-peer,architecture system, for example. The various systems of theapplications server(s) 140 (e.g., the publication system(s) 142 and thepayment system(s) 144) can also be implemented as standalone softwareprograms, which do not necessarily have networking capabilities.

FIG. 2 illustrates a block diagram showing components provided withinthe publication system(s) 142, according to some embodiments. In variousexample embodiments, the publication system(s) 142 comprises a marketplace system to provide market place functionality (e.g., facilitatingthe purchase of items associated with item listings on an e-commercewebsite). The publication system(s) 142 can be hosted on dedicated orshared server machines that are communicatively coupled to enablecommunications between server machines. The components themselves arecommunicatively coupled (e.g., via appropriate interfaces) to each otherand to various data sources, so as to allow information to be passedbetween the applications or so as to allow the applications to share andaccess common data. Furthermore, the components access one or moredatabase(s) 126 via the database server(s) 124.

The publication system(s) 142 provides a number of publishing, listing,and price-setting mechanisms whereby a seller (also referred to as a“first user”) may list (or publish information concerning) goods orservices for sale or barter, a buyer (also referred to as a “seconduser”) can express interest in or indicate a desire to purchase orbarter such goods or services, and a transaction (such as a trade) maybe completed pertaining to the goods or services. To this end, thepublication system(s) 142 comprises a publication engine 210 and aselling engine 220, according to some embodiments. The publicationengine 210 publishes information, such as item listings or productdescription pages, on the publication system(s) 142. In someembodiments, the selling engine 220 comprises one or more fixed-priceengines that support fixed-price listing and price setting mechanismsand one or more auction engines that support auction-format listing andprice setting mechanisms (e.g., English, Dutch, Chinese, Double, Reverseauctions, etc.). The various auction engines can also provide a numberof features in support of these auction-format listings, such as areserve price feature whereby a seller specifies a reserve price inconnection with a listing and a proxy-bidding feature whereby a biddermay invoke automated proxy bidding. The selling engine 220 can furthercomprise one or more deal engines that support merchant-generated offersfor products and services.

A listing engine 230 allows sellers to conveniently author listings ofitems or authors to author publications. In one embodiment, the listingspertain to goods or services that a user (e.g., a seller) wishes totransact via the networked system 102. In some embodiments, the listingscan be an offer, deal, coupon, or discount for the good or service. Eachgood or service is associated with a particular category. The listingengine 230 receives listing data such as title, description, and aspectname/value pairs. Furthermore, each listing for a good or service can beassigned an item identifier. In other embodiments, a user may create alisting that is an advertisement or other form of informationpublication. The listing information may then be stored to one or morestorage devices coupled to the networked system 102 (e.g., database(s)126). Listings also can comprise product description pages that displaya product and information (e.g., product title, specifications, andreviews) associated with the product. In some embodiments, the productdescription page includes an aggregation of item listings thatcorrespond to the product described on the product description page.

The listing engine 230 also may allow buyers to conveniently authorlistings or requests for items desired to be purchased. In someembodiments, the listings may pertain to goods or services that a user(e.g., a buyer) wishes to transact via the networked system 102. Eachgood or service is associated with a particular category. The listingengine 230 receives as much or as little listing data, such as title,description, and aspect name/value pairs, that the buyer is aware ofabout the requested item. In some embodiments, the listing engine 230parses the buyer's submitted item information and completes incompleteportions of the listing. For example, if the buyer provides a briefdescription of a requested item, the listing engine 230 parses thedescription, extracts key terms, and uses those terms to make adetermination of the identity of the item. Using the determined itemidentity, the listing engine 230 retrieves additional item details forinclusion in the buyer item request. In some embodiments, the listingengine 230 assigns an item identifier to each listing for a good orservice.

In some embodiments, the listing engine 230 allows sellers to generateoffers for discounts on products or services. The listing engine 230 canreceive listing data, such as the product or service being offered, aprice or discount for the product or service, a time period for whichthe offer is valid, and so forth. In some embodiments, the listingengine 230 permits sellers to generate offers from sellers' mobiledevices. The generated offers can be uploaded to the networked system102 for storage and tracking.

Searching the publication system(s) 142 is facilitated by a searchingengine 240. For example, the searching engine 240 enables keywordqueries of listings published via the publication system(s) 142. Inexample embodiments, the searching engine 240 receives the keywordqueries from a device (e.g., client device 110) of a user (e.g., user106) and conducts a review of the storage device storing the listinginformation. The review will enable compilation of a result set oflistings that can be sorted and returned to the client device 110 of theuser. The searching engine 240 can record the query (e.g., keywords) andany subsequent user actions and behaviors (e.g., navigations,selections, or click-throughs).

The searching engine 240 also can perform a search based on a locationof the user. A user may access the searching engine 240 via a mobiledevice and generate a search query. Using the search query and theuser's location, the searching engine 240 returns relevant searchresults for products, services, offers, auctions, and so forth to theuser. The searching engine 240 can identify relevant search results bothin list form and graphically on a map. Selection of a graphicalindicator on the map can provide additional details regarding theselected search result. In some embodiments, the user specifies, as partof the search query, a radius or distance from the user's currentlocation to limit search results.

In a further example, a navigation engine 250 allows users to navigatethrough various categories, catalogs, or inventory data structuresaccording to which listings may be classified within the publicationsystem(s) 142. For example, the navigation engine 250 allows a user tosuccessively navigate down a category tree comprising a hierarchy ofcategories (e.g., the category tree structure) until a particular set oflistings is reached. Various other navigation applications within thenavigation engine 250 can be provided to supplement the searching andbrowsing applications. The navigation engine 250 can record the varioususer actions (e.g., clicks) performed by the user in order to navigatedown the category tree.

In some embodiments, a personalization engine 260 provides functionalityto personalize various aspects of user interactions with the networkedsystem 102. For instance, the user can define, provide, or otherwisecommunicate personalization settings used by the personalization engine260 to determine interactions with the publication system(s) 142. Infurther example embodiments, the personalization engine 260 determinespersonalization settings automatically and personalizes interactionsbased on the automatically determined settings. For example, thepersonalization engine 260 determines a native language of the user andautomatically presents information in the native language.

FIG. 3 is a block diagram of the cannibalization regulation system 150that provides functionality to improve advertisement revenue whilemaintaining a specified or dynamically determined level ofcannibalization. In an example embodiment, the cannibalizationregulation system 150 includes a presentation module 310, acommunication module 320, data module 330, and an analysis module 340.All, or some, of the modules 310-340 of FIG. 3 communicate with eachother, for example, via a network coupling, shared memory, and the like.It will be appreciated that each module can be implemented as a singlemodule, combined into other modules, or further subdivided into multiplemodules. Other modules not pertinent to example embodiments can also beincluded, but are not shown.

In some implementations, the presentation module 310 provides variouspresentation and user interface functionality operable to interactivelypresent (or cause presentation) and receive information from the user.For instance, the presentation module 310 can cause presentation of anadvertisement on a user interface of a user device. In variousimplementations, the presentation module 310 presents or causespresentation of information (e.g., visually displaying information on ascreen, acoustic output, haptic feedback). Interactively presentinginformation is intended to include the exchange of information between aparticular device and the user. The user may provide input to interactwith the user interface in many possible manners such as alphanumeric,point based (e.g., cursor), tactile, or other input (e.g., touch screen,tactile sensor, light sensor, infrared sensor, biometric sensor,microphone, gyroscope, accelerometer, or other sensors), and the like.It will be appreciated that the presentation module 310 provides manyother user interfaces to facilitate functionality described herein.Further, it will be appreciated that “presenting” as used herein isintended to include communicating information or instructions to aparticular device that is operable to perform presentation based on thecommunicated information or instructions.

The communication module 320 provides various communicationsfunctionality and web services. For example, the communication module320 provides network communication such as communicating with thenetworked system 102, the client device 110, and the third partyserver(s) 130. In various example embodiments, the network communicationcan operate over wired or wireless modalities. Web services are intendedto include retrieving information from the third party server(s) 130,the database(s) 126, and the application server(s) 140. In someembodiments, the communication module 320 receives information from theclient device 110 such as advertisement parameters or metrics resultingfrom presented advertisements (e.g., whether the user clicked on aparticular advertisement, or a number of advertisement impressions aparticular user or client device has viewed).

The data module 330 provides functionality to access historical data andcurrent data, each of which include, for example, advertisement revenue,advertisement parameters, one or more cannibalization metrics, and otherdata. In some embodiments, the historical data and the current data canbe stored in the database(s) 126 and accessed by the data module 330. Invarious embodiments, the data module 330 stores the advertisementrevenue, advertisement parameters, and cannibalization metric in thedatabase(s) 126.

The analysis module 340 provides functionality to analyze the historicaldata and the current data to determine the value for at least oneadvertisement parameter that causes a desired advertisement revenue(e.g., a maximum advertisement revenue or otherwise optimizedadvertisement revenue) while maintaining a certain level ofcannibalization. For example, the analysis module 340 generates variousmodels that are employed by the analysis module 340 to determine thevalue for at least one of the advertisement parameters with respect to abounded cannibalization metric.

FIG. 4 is a block diagram illustrating an example method 400 foradvertising cannibalization management. Data 402 includes historicaldata 404 and current data 414. In various embodiments, the historicaldata 404 and the current data 414 each include advertisement revenue,advertisement parameters, and one or more cannibalization metrics. Insome embodiments, the data 402 is stored in the database(s) 126accessible by the data module 330. The advertisement parameters include,for example, advertisement placement, impressions, clicks, page views,and other parameters. The advertisement revenue is revenue generated asa result of advertisements (e.g., interactive advertisements on awebsite being displayed or presented in a browser or in a mobileapplication on a mobile computing device). The one or morecannibalization metrics can comprise purchase per user per week or thebought items (herein, referred to as PPW) or gross merchandise per userper week or the dollar amount of the bought items (herein, referred toas GPW).

In various embodiments, the analysis module 340 generates a revenuemodel 406 that models the advertisement revenue with respect to theadvertisement parameters. In some embodiments, the revenue model 406 isa polynomial model or another type of model that forms a mathematicalrelationship between advertisement revenue and the advertisementparameters (e.g., advertisement revenue can be calculated, estimated, orapproximated given advertisement parameters and the advertisementrevenue model 406). For example, the advertisement revenue can beinfluenced by the impressions or placement (e.g., a higher number ofadvertisement impressions can result in a higher advertisement revenue).

The analysis module 340 identifies a cannibalization covariate 408 amongcandidate covariates (e.g., the advertisement parameters). For instance,an operator, manager, or administrator of the cannibalization regulationsystem 150 specified the cannibalization covariate among the candidatecovariates or the cannibalization regulation system 150 automaticallydetermines the cannibalization covariate 408 among the candidatecovariates. The cannibalization covariate is predictive of or correlatedwith the sales cannibalization (e.g., as the sales cannibalizationincreases, the cannibalization covariate 408 increases or otherwisechanges). For example, the impressions may be correlated with the salescannibalization since a higher number of advertisement impressions canincrease the probability for a sales cannibalization. That is to say, asmore advertisements are shown to a particular user, it is more likelythat the particular user may click on an advertisement and a potentialsale to the particular user may be lost. Thus, the impressionsadvertisement parameter can be correlated or predictive of salescannibalization.

Once the analysis module 340 identifies the cannibalization covariate408, the analysis module 340 generates a covariate model 410. Forinstance, the covariate model is a polynomial model or another type ofmodel that forms a mathematical relationship between the cannibalizationcovariate and the advertisement parameters (e.g., the cannibalizationcovariate can be calculated, estimated, or approximated given theadvertisement parameters and the covariate model 410). The covariatemodel 410 models the cannibalization covariate with respect to theadvertisement parameters. For instance, the covariate model 410indicates a behavior of the cannibalization covariate 408 with respectto changes in the advertisement parameters. For instance, the covariatemodel 410 indicates a relationship between the placement of a particularadvertisement and the cannibalization covariate 408 (e.g., a particularadvertisement placement may increase or decrease the cannibalizationcovariate 408).

The analysis module 340 generates a cannibalization model 412 thatmodels the cannibalization metric with respect to the cannibalizationcovariate 408. Similar to the revenue model discussed above, in someembodiments, the cannibalization model 412 is a polynomial model oranother type of model that forms a mathematical relationship betweencannibalization metric and the cannibalization covariate (e.g., aparticular cannibalization metric can be calculated, estimated, orapproximated given the cannibalization covariate and the cannibalizationmodel 412 and vice versa). For example, the cannibalization model 412indicates a relationship between the cannibalization covariate 408 andthe cannibalization metric. The cannibalization model 412 allows forcomparisons between the cannibalization covariate 408 and thecannibalization metric.

After the analysis module 340 identifies the cannibalization covariate408, generates the revenue model 406, generates the covariate model 410,and generates the cannibalization model 412, at block 416, the analysismodule 340 determines a value for at least one advertisement parameterthat causes a desired advertisement revenue (e.g., a highestadvertisement revenue) with a bounded or otherwise constrainedcannibalization metric.

In various embodiments, the analysis module 340 uses the cannibalizationmodel 412 to determine a lower limit and an upper limit for the boundedcannibalization metric. In these embodiments, an operator, manager, oradministrator of the cannibalization regulation system 150 can specifythe upper limit and lower limit in terms of the cannibalization metric.The analysis module 340 can convert the upper limit and the lower limitspecified in terms of the cannibalization metric to the upper limit andthe lower limit in terms of the cannibalization covariate 408 using thecannibalization model 412 (e.g., inputting the cannibalization metricinto the cannibalization model 412 to calculate, estimate, orapproximate the a value in terms of the cannibalization covariate).

In further example embodiments, the analysis module 340 determines thelower limit for the bounded cannibalization metric according to aminimum advertisement revenue specified by the operator (e.g.,determined by the analysis module 340 using the revenue model 406, thecovariate model 410, and the cannibalization model 412). In anotherexample embodiment, the analysis module 340 determines the upper limitof the bounded cannibalization metric according to a maximumcannibalization cost specified by the operator (e.g., determined by theanalysis module 340 using the revenue model 406, the covariate model410, and the cannibalization model 412). The cannibalization cost is anamount of lost sales revenue resulting from presentation ofadvertisements.

Subsequent to bounding the cannibalization metric, the analysis module340 determines the value for at least one of the advertisementparameters using the revenue model 406 in conjunction with thecannibalization covariate 408 that is constrained by the upper limit andthe lower limit specified in terms of the cannibalization covariate 408(example equations for the analysis module 340 determining the value forat least one of the advertisement parameters are discussed below). In anembodiment, the analysis module 340 determines the value for at leastone of the advertisement parameters such that, when used (e.g.,presenting advertisements according to the determined value), it causesa desired advertisement revenue (e.g., a maximum advertisement revenueor another advertisement revenue specified by an operator of thecannibalization regulation system 150) of the revenue model 406 whilemaintaining the cannibalization covariate 408 within the upper limit andlower limit bounds.

Once the analysis module 340 determines the value for at least one ofthe advertisement parameters, at block 418 the presentation module 310causes presentation of an advertisement using the determined value. Forexample, the determined value may be a number of impressions, anadvertisement placement, another advertisement parameter, or a suitablecombination thereof. After the presentation module 340 causespresentation of the advertising using the determined value, various data(e.g., sales or clicks on advertisements) can be observed or otherwiseobtained by the cannibalization regulation system 150. For instance,observed data 420 resulting from a presentation of the advertisementusing the determined value is stored by the data module 330 forsubsequent analysis. For instance, particular settings for theadvertisement parameters may produce certain clicks, sales, and so forththat can be stored for subsequent analysis by the cannibalizationregulation system 150.

In some embodiments, the analysis module 340 uses the current data 414in conjunction with the historical data 404 to determine a change amountfor at least one of the advertisement parameters. For example, thecurrent data 414 can include advertisement revenue, advertisementparameters, and one or more cannibalization metrics for a time period ofa duration (e.g., one day). The analysis module 340 accesses currentadvertisement parameters from the current data and determines the changeamount corresponding to at least one of the current advertisementparameters. In this example, the change amount, when used, causes adesired advertisement revenue (e.g., a maximum advertisement revenue)while maintaining the bounded cannibalization metric. In this way, theanalysis module 340 improves, enhances, or causes the desiredadvertisement revenue for a rolling period of time as there may not be aglobal optimization or maximization (e.g., a different optimization ormaximization for each new day or each new time period of the duration).

FIG. 5 is a flow diagram illustrating an example method 500 foradvertising cannibalization management. The operations of the method 500can be performed by components of the cannibalization regulation system150, and are so described below for the purpose of illustration.

At operation 510, the data module 330 accesses historical data andcurrent data. For example, the data module 330 assesses the historicaldata (e.g., historical data 404) and the current data (e.g., currentdata 414) from the database(s) 126. The historical data and the currentdata each include advertisement revenue, advertisement parameters, andone or more cannibalization metrics. The current data pertains to arecent time period of time (e.g., yesterday, or the last hour). Forinstance, the recent time period can be a time period starting at thepresent time and extending a certain duration in the past (e.g., onehour, one day, one week).

At operation 520, the analysis module 340 determines the value for atleast one of the advertisement parameters by analyzing the historicaldata. The determined value, when used, causes a desired advertisementrevenue (e.g., a maximum advertisement revenue or another specifiedadvertisement revenue) with respect to a bounded cannibalization metric.That is to say, the analysis module 340 determines the value for atleast one of the advertisement parameters (e.g., placement orimpressions) such that advertisement revenue is maximized, optimized, orspecified while maintaining a certain level of cannibalization (e.g., asdetermined by the cannibalization metric). In some embodiments, thelevel of cannibalization is specified by an operator, administration, ormanager of the cannibalization regulation system 150.

Referring now to FIG. 6, a flow diagram illustrating example operationsfor determining an advertisement parameter is shown. Subsequent to thedata module 330 accessing the historical data at the operation 510, theanalysis module 340 determines a value for at least one of theadvertisement parameters by analyzing the historical data at theoperation 520. In some embodiments, the operation 520 includes theadditional operations of FIG. 6.

At operation 610, the analysis module 340 identifies the cannibalizationcovariate from among candidate covariates (e.g., advertisementparameters such as clicks, impressions, page views). For instance, thecannibalization covariate is a particular advertisement parameter thatis correlated with or predictive of sales cannibalization.

Referring now to FIG. 7, a flow diagram illustrating example operationsfor identifying the cannibalization covariate is shown. At the operation610, the analysis module 340 identifies the cannibalization covariate.In some embodiments, the operation 610 includes the additionaloperations of FIG. 7.

At operation 710, the analysis module 340 measures a cannibalizationvalue by comparing cannibalization of a control group of users shownadvertisements (e.g., shown advertisements during a browsing session onan e-commerce website or in an e-commerce app on a mobile computingdevice) and a treatment group of users not shown advertisements. Forexample, the cannibalization value can be a comparison in sales revenue,sales count, or another metric between the control group and thetreatment group (e.g., a percentage difference in sales). In an example,the difference in sales between the treatment group and the controlgroup is attributed to sales cannibalization resulting fromadvertisements being show to the control group. In some instances, datafrom the control group and the treatment group are collected in parallel(e.g., collected the same time or nearly the same time) to minimizeeffects that can cause a difference in sales revenue other than theadvertisement parameters. That is to say, the treatment group of userscan have a higher number of sales as compared to the control group ofusers since the control group of users is shown advertisements (e.g.,the advertisements in the control group are causing salescannibalization). In some embodiments, the analysis module 340 canemploy a plurality of control groups and treatment groups to measure aplurality of cannibalization values (e.g., measurements of PPW or GPW)for different groups. Each group of the plurality of control groups andtreatment groups can be shown advisements with different advertisementparameters to help identify various effects of the advertisementparameters on sales.

At operation 720, the analysis module 340 identifies the cannibalizationcovariate from among the candidate covariates (e.g., the advertisementparameters) according to a correlation between respective candidatecovariates and the measured cannibalization value. In some embodiments,the cannibalization covariate is a highest correlated candidatecovariate among the candidate covariates. For example, clicks, oranother advertisement parameter, may be highly correlated with the salescannibalization caused by advertisements (e.g., when a user clicks on anadvertisement there is a high likelihood that sales from that user arelost as a result of the advertisement being presented to the user).

Referring back to FIG. 6, at operation 620, the analysis module 340generates the covariate model that models the cannibalization covariatewith respect to the advertisement parameters. For example, the covariatemodel can indicate relationships between the cannibalization covariateand advertisement parameters such as placement and impressions.

In a specific example, the analysis module 340 uses a coefficient matrixfor regression as follows:

β=[b _(o) b _(p) b _(Q)]′

The coefficients can be used in the covariate model. In these exampleequations, placement is denoted by “p” and impressions “Q.” In thisexample, the analysis module 340 models the cannibalization covariate asfollows:

cannibalization covariate, f={hacek over (X)}β=b _(o) +b _(p) p+b _(Q)p⊙Q

In various embodiments, the analysis module 340 uses the historical datato calculate or estimate β.

At operation 630, the analysis module 340 generates a cannibalizationmodel that models the cannibalization metric (e.g., PPW or GPW) withrespect to the cannibalization covariate. For example, the analysismodule 340 generates a regression model with the cannibalization metricas a response variable and the cannibalization covariate as thepredictor variable. In various embodiments, the analysis module 340 usesthe cannibalization model for variable transformation or conversion. Forinstance, the analysis module 340 can convert a variable that isspecified in terms of the cannibalization metric to a variable specifiedin terms of the advertisement parameters using the cannibalization modeland the covariate model.

In some example embodiments, an operator, manager, or administrator ofthe cannibalization regulation system 150 specifies the cannibalizationto be either PPW or GPW. For example, depending upon the businessstrategy, as determined by the administrator, the analysis module 340can employ PPW or GPW as the cannibalization metric.

At operation 640, the analysis module 340 generates a revenue model thatmodels the advertisement revenue with respect to the advertisementparameters. As described above, the advertisement parameters can includean advertisement placement, impressions, clicks, advertiser, anadvertisement type, or other parameters. In various embodiments, therevenue model provides an approximation, estimation, or prediction ofthe advertisement revenue based on the advertisement parameters.

In a specific example, predictor variables can include advertisementparameters such as placement (p) and impressions (Q).

X=[pQ]

In this example, the analysis module 340 uses the regression modelmatrix that includes placements' main effect and interaction effect withthe impressions.

{hacek over (X)}=[1pp⊙Q]

In this equation, ⊙ represents element by element multiplication ofmatrices or vectors. In this example, the analysis module 340 uses thecoefficient matrix for modeling advertisement revenue as follows:

α=[a _(o) a _(P) a _(Q)]′

These coefficients can be used in the revenue model as shown below. Inthis example, the analysis module 340 models the advertisement revenueas follows:

advertisement revenue, g={hacek over (X)}α=a _(o) +a _(p) p+a _(Q) p⊙Q

In various embodiments, the analysis module 340 uses the historical datato estimate α.

Subsequent to the analysis module 340 identifying the cannibalizationcovariate, generating the covariate model, generating thecannibalization model, and generating the revenue module, at operation650, the analysis module 340 determines the value for at least one ofthe advertisement parameters using the revenue model in conjunction withthe cannibalization model and the covariate model.

In various example embodiments, there may not be a global optimizationor maximization for the advertisement parameters that optimizes ormaximizes advertisement revenue with a bounded cannibalization metric(e.g., a particular advertisement parameter value for one time periodmay not be optimal for a different time period). In other words, thedetermined value for a particular advertisement parameter can changewith time. To mitigate issues associated with a non-global optimization,the analysis module 340 can determine the value for at least one of theadvertisement parameters for a specified time period (e.g., one day orone hour) using the current data in conjunction with the historicaldata. In these embodiments, the analysis module 340 determines a changeamount for at least one of the advertisement parameters for thespecified time period by analyzing the historical data in conjunctionwith the current data. Subsequently, the presentation module 310 causespresentation of the advertisement using the change amount (e.g., adjuststhe advertisement parameters by the optimal change amount).

In a specific example, the analysis module 340 determines the changeamount relative to the current state (as represented by the currentdata) to cause the desired change in cannibalization (e.g., maintainingthe cannibalization within bounds). In this example, the analysis module340 can find a gradient of the cannibalization covariate and theadvertisement revenue with respect to a change in X. Although X can bediscrete, the analysis module 340, in some instances, can consider X tobe continuous to find the derivatives in the following equation.

${\nabla f} = {\frac{f}{X} = \begin{bmatrix}{b_{p} + {Q^{\prime} \odot b_{Q}}} & {p^{\prime} \odot b_{Q}}\end{bmatrix}}$

In this example, for a change in X (ΔX), a change in clicks can bedescribed as follows:

${{\Delta \; f} = {{{\nabla f}\; \Delta \; X} = {{\begin{bmatrix}{b_{p} + {Q^{\prime} \odot b_{Q}}} & {p \odot b_{Q}}\end{bmatrix}\begin{bmatrix}{\Delta \; p} \\{\Delta \; Q}\end{bmatrix}} = {{{b_{p}\Delta \; p} + {{Q^{\prime} \odot b_{Q}}\Delta \; p} + {{p \odot b_{Q}}\Delta \; Q}} = {(I) + ({II}) + ({III})}}}}},$

In this equation, (I) is a change resulting from removing a placement(e.g., a change in the placement advertising parameter), (II) is areduction or increase in a number of impressions resulting from removingthe placement, (III) is a change resulting from a change in the numberof impressions.

In this example, the analysis module 340 determines the advertisementrevenue as follows:

Δg=a _(p) Δp+Q′⊙a _(Q) Δp+p⊙a _(Q) ΔQ

Subsequent to the analysis module 340 determining the above functions,the analysis module 340 formulates a mixed integer optimization model.For example, at optimization time period t, the analysis module 340 usesX^((t-1)) to find the change amount in X for time period t or ΔX^((t)).

max Δg=a _(p) p ^((t)) +Q′ ^((t-1)) ⊙a _(Q) Δp ^((t)) p′ ^((t-1)) ⊙a_(Q) ΔQ ^((t))

Such that:

c ₁ ≦ΔfΔX ^((t)) =b _(p) Δp ^((t)) +Q′ ^((t-1)) ⊙b _(Q) Δp ^((t)) +p′^((t-1)) ⊙b _(Q) ΔQ ^((t)) ≦c ₂  (I)

ΔQ _(i) ^((t))(1−(p _(i) ^((t-1)) +Δp _(i) ^((t))))=0;∀i  (II)

Δp _(i) ^((t))ε{−1,0,1};∀i  (III)

p _(i) ^((t-1)) +Δp _(i) ^((t))ε{0,1};∀i  (IV)

Q _(i) ^((t-1)) +ΔQ _(i) ^((t))≧0;∀i  (V)

ΔQ _(i) ^((t)) εI;∀i  (VI)

In these example equations, constraint (I) is for constraining thechange in the cannibalization covariate within desired limits (e.g., theupper limit and the lower limit). Constraint (II) is to set a change inimpressions to zero if off placement is kept off or an on placement isturned off (e.g., a particular state of an advertisement placement, suchas on or off, is not changed). For constraint (III), can be set tonegative one, zero, or one. For the constraint (III), negative oneindicates removing an on placement, zero indicates do nothing, and oneindicates to switch on a placement. Constraint (IV) is for not turningoff an already off placement or turning on an already on placement.Constraint (V) is to prevent a reduction in a number of impressionsgreater than the current number of impressions. In some embodiments,constraint (VI) is to constrain results to a set of integers.

In some example embodiments, the constraints described above are revisedto allow for a linear optimization as opposed to a non-linearoptimization, which can improve computational performance of theanalysis module 340. For example, in the constraints above, constraint(II) is non-linear. The analysis module 340 removing the constraint (II)can allow for linear optimization.

In an embodiment, the analysis module 340 can use substitute constraintsto replace, for example, constraints (II) and (V) and combine them intoa single linear constraint as follows:

max Δg=a _(p) Δp ^((t)) +Q′ ^((t-1)) ⊙a _(Q) Δp ^((t)) +p′ ^((t-1)) ⊙a_(Q) ΔQ ^((t))

Such that:

c ₁ ≦ΔfΔX ^((t)) =b _(p) Δp ^((t)) +Q′ ^((t-1)) ⊙b _(Q) Δp ^((t)) +p′^((t-1)) ⊙b _(Q) ΔQ ^((t)) ≦c ₂  (I)

−Q _(i) ^((t-1))(p _(i) ^((t-1)) +Δp _(i) ^((t)))≦ΔQ _(i) ^((t)) ≦Q _(i)^((t-1))(p _(i) ^((t-1)) +Δp _(i) ^((t)));∀i  (II′)

Δp _(i) ^((t))ε{−1,0,1};∀i  (III)

p _(i) ^((t-1)) +Δp _(i) ^((t))ε{0,1};∀i  (IV)

ΔQ _(i) ^((t)) εI;∀i  (V)

In these example equations, i is the index for a particular placement.The revised constraint (II′) is for setting a change in a number ofimpressions equal to zero if off placement is kept off or an onplacement is turned off. In addition, the change in the number ofimpressions may not be more than the current number of impressions.

Referring back to FIG. 5, at operation 530, subsequent to the analysismodule 340 determining the value for at least one of the advertisementparameters, the presentation module 310 causes presentation, in realtime, of the advertisement on the user interface of the client device(e.g., client device 110) using the determined value. In otherembodiments, the presentation module 310 causes presentation of theadvertisement on the user interface of the client device using thedetermined change amount for another time period of the duration. Theterm “real time,” as used herein, is intended to include presentingafter a short delay. For example, the presentation module 310communicating instructions to the client device 110 to causepresentation of the advertisement using the determined value may bepresented on the client device after a delay interval (e.g., due totransmission delay or other delays such as data being temporarily storedat an intermediate device). This discussion of real time applies equallythroughout the specification in relation to other uses of the term “realtime.”

Once the presentation module 310 causes presentation of theadvertisement on the user interface of the client device, in someembodiments, the communication module 320 receives data associated withthe advertisement (e.g., did the user click on the advertisement or didthe user make a purchase at the website). The data module 330 can storedata associated with the advertisement presentation in the database(s)126 for subsequent analysis. In this way, the cannibalization regulationsystem 150 can continuously optimize or maximize the advertisementrevenue while constraining the cannibalization using the current dataand the historical data.

Example User Interfaces

FIGS. 8-9 are user interface diagrams depicting various example userinterfaces for interactively presenting information to the user.Although FIGS. 8-9 depict specific example user interfaces and userinterface elements, these are merely non-limiting examples; many otheralternate user interfaces and user interface elements can be generatedby the presentation module 310 and presented to the user. It will benoted that alternate presentations of the displays of FIGS. 8-9 includeadditional information, graphics, options, and so forth; otherpresentations include less information, or provide abridged informationfor easy use by the user.

FIG. 8 is a user interface diagram depicting an example user interface800. The user interface 800 includes an advertisement 810 correspondingto advertisement parameters 820. The user interface 800 also includes aproduct for sale 830. The user can interact with the user interface 800by for example clicking on the advertisement 810 or clicking on theproduct for sale 830. Such interactions can be observed or otherwiseobtained by the cannibalization regulation system 150 for analysis.

In this example, the advertisement parameters 820 include placement,impression, size, advertiser, type, and so on. The presentation module310 can cause presentation of the advertisement 810 according to variousadvertisement parameters including the determined value for at least oneof the advertisement parameters. For instance, if the analysis module340 determines the value for the impressions, the presentation module310 can cause presentation of the advertisement 810 according to thevalue of the impressions.

FIG. 9 is a user interface diagram 900 that shows an example userinterface 910 of a mobile computing device (e.g., a smart phone). Theexample user interface 910 includes user interface element 920 and anadvertisement 930 corresponding to advertisement parameters 940. Theuser can interact with the various user interface elements of the userinterface 910 by tapping on a particular user interface element (e.g.,tactile input via a touch screen interface on a mobile device). Forinstance, the user can sort or otherwise navigate the items for saleincluded in the user interface 910 using the user interface element 920.

The advertisement parameters include placement, impression, size,advertiser, type, and so on. The presentation module 310 can causepresentation of the advertisement 930 according to various advertisementparameters (e.g., size, position, or placement) including the value forat least one of the advertisement parameters. For instance, if theanalysis module 340 determines the value for the impressions, thepresentation module 310 can cause presentation of the advertisement 930according to the value of the impressions.

Modules, Components, and Logic

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. Modules can constitute eithersoftware modules (e.g., code embodied on a machine-readable medium or ina transmission signal) or hardware modules. A “hardware module” is atangible unit capable of performing certain operations and can beconfigured or arranged in a certain physical manner. In various exampleembodiments, one or more computer systems (e.g., a standalone computersystem, a client computer system, or a server computer system) or one ormore hardware modules of a computer system (e.g., a processor or a groupof processors) is configured by software (e.g., an application orapplication portion) as a hardware module that operates to performcertain operations as described herein.

In some embodiments, a hardware module is implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware module can include dedicated circuitry or logic that ispermanently configured to perform certain operations. For example, ahardware module can be a special-purpose processor, such as aField-Programmable Gate Array (FPGA) or an Application SpecificIntegrated Circuit (ASIC). A hardware module may also includeprogrammable logic or circuitry that is temporarily configured bysoftware to perform certain operations. For example, a hardware modulecan include software encompassed within a general-purpose processor orother programmable processor. It will be appreciated that the decisionto implement a hardware module mechanically, in dedicated andpermanently configured circuitry, or in temporarily configured circuitry(e.g., configured by software) can be driven by cost and timeconsiderations.

Accordingly, the phrase “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. As used herein,“hardware-implemented module” refers to a hardware module. Consideringembodiments in which hardware modules are temporarily configured (e.g.,programmed), each of the hardware modules need not be configured orinstantiated at any one instance in time. For example, where a hardwaremodule comprises a general-purpose processor configured by software tobecome a special-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware modules) at different times. Software canaccordingly configure a particular processor or processors, for example,to constitute a particular hardware module at one instance of time andto constitute a different hardware module at a different instance oftime.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules can be regarded as being communicatively coupled. Where multiplehardware modules exist contemporaneously, communications can be achievedthrough signal transmission (e.g., over appropriate circuits and buses)between or among two or more of the hardware modules. In embodiments inwhich multiple hardware modules are configured or instantiated atdifferent times, communications between such hardware modules may beachieved, for example, through the storage and retrieval of informationin memory structures to which the multiple hardware modules have access.For example, one hardware module performs an operation and stores theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware module can then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules can also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein can beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors constitute processor-implemented modulesthat operate to perform one or more operations or functions describedherein. As used herein, “processor-implemented module” refers to ahardware module implemented using one or more processors.

Similarly, the methods described herein can be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method can be performed by one or more processors orprocessor-implemented modules. Moreover, the one or more processors mayalso operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an Application ProgramInterface (API)).

The performance of certain of the operations may be distributed amongthe processors, not only residing within a single machine, but deployedacross a number of machines. In some example embodiments, the processorsor processor-implemented modules are located in a single geographiclocation (e.g., within a home environment, an office environment, or aserver farm). In other example embodiments, the processors orprocessor-implemented modules are distributed across a number ofgeographic locations.

Software Architecture

FIG. 10 is a block diagram 1000 illustrating an architecture of software1002, which can be installed on any one or more of the devices describedabove. FIG. 10 is merely a non-limiting example of a softwarearchitecture, and it will be appreciated that many other architecturescan be implemented to facilitate the functionality described herein. Invarious embodiments, the software 1002 is implemented by hardware suchas machine a 1100 of FIG. 11 that includes processors 1110, memory 1130,and I/O components 1150. In this example architecture, the software 1002can be conceptualized as a stack of layers where each layer may providea particular functionality. For example, the software 1002 includeslayers such as an operating system 1004, libraries 1006, frameworks1008, and applications 1010. Operationally, the applications 1010 invokeapplication programming interface (API) calls 1012 through the softwarestack and receive messages 1014 in response to the API calls 1012,consistent with some embodiments.

In various implementations, the operating system 1004 manages hardwareresources and provides common services. The operating system 1004includes, for example, a kernel 1020, services 1022, and drivers 1024.The kernel 1020 acts as an abstraction layer between the hardware andthe other software layers consistent with some embodiments. For example,the kernel 1020 provides memory management, processor management (e.g.,scheduling), component management, networking, and security settings,among other functionality. The services 1022 can provide other commonservices for the other software layers. The drivers 1024 are responsiblefor controlling or interfacing with the underlying hardware, accordingto some embodiments. For instance, the drivers 1024 can include displaydrivers, camera drivers, BLUETOOTH® drivers, flash memory drivers,serial communication drivers (e.g., Universal Serial Bus (USB) drivers),WI-FI® drivers, audio drivers, power management drivers, and so forth.

In some embodiments, the libraries 1006 provide a low-level commoninfrastructure utilized by the applications 1010. The libraries 1006 caninclude system libraries 1030 (e.g., C standard library) that canprovide functions such as memory allocation functions, stringmanipulation functions, mathematic functions, and the like. In addition,the libraries 1006 can include API libraries 1032 such as medialibraries (e.g., libraries to support presentation and manipulation ofvarious media formats such as Moving Picture Experts Group-4 (MPEG4),Advanced Video Coding (H.264 or AVC), Moving Picture Experts GroupLayer-3 (MP3), Advanced Audio Coding (AAC), Adaptive Multi-Rate (AMR)audio codec, Joint Photographic Experts Group (JPEG or JPG), or PortableNetwork Graphics (PNG)), graphics libraries (e.g., an OpenGL frameworkused to render in two dimensions (2D) and three dimensions (3D) in agraphic content on a display), database libraries (e.g., SQLite toprovide various relational database functions), web libraries (e.g.,WebKit to provide web browsing functionality), and the like. Thelibraries 1006 can also include a wide variety of other libraries 1034to provide many other APIs to the applications 1010.

The frameworks 1008 provide a high-level common infrastructure that canbe utilized by the applications 1010, according to some embodiments. Forexample, the frameworks 1008 provide various graphic user interface(GUI) functions, high-level resource management, high-level locationservices, and so forth. The frameworks 1008 can provide a broad spectrumof other APIs that can be utilized by the applications 1010, some ofwhich may be specific to a particular operating system or platform.

In an example embodiment, the applications 1010 include a homeapplication 1050, a contacts application 1052, a browser application1054, a book reader application 1056, a location application 1058, amedia application 1060, a messaging application 1062, a game application1064, and a broad assortment of other applications such as a third partyapplication 1066. According to some embodiments, the applications 1010are programs that execute functions defined in the programs. Variousprogramming languages can be employed to create one or more of theapplications 1010, structured in a variety of manners, such asobject-oriented programming languages (e.g., Objective-C, Java, or C++)or procedural programming languages (e.g., C or assembly language). In aspecific example, the third party application 1066 (e.g., an applicationdeveloped using the ANDROID™ or IOS™ software development kit (SDK) byan entity other than the vendor of the particular platform) may bemobile software running on a mobile operating system such as IOS™,ANDROID™, WINDOWS® PHONE, or another mobile operating systems. In thisexample, the third party application 1066 can invoke the API calls 1012provided by the operating system 1004 to facilitate functionalitydescribed herein.

Example Machine Architecture and Machine-Readable Medium

FIG. 11 is a block diagram illustrating components of a machine 1100,according to some embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.Specifically, FIG. 11 shows a diagrammatic representation of the machine1100 in the example form of a computer system, within which instructions1116 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1100 to perform any oneor more of the methodologies discussed herein can be executed. Inalternative embodiments, the machine 1100 operates as a standalonedevice or can be coupled (e.g., networked) to other machines. In anetworked deployment, the machine 1100 may operate in the capacity of aserver machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 1100 can comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a set-top box (STB), apersonal digital assistant (PDA), an entertainment media system, acellular telephone, a smart phone, a mobile device, a wearable device(e.g., a smart watch), a smart home device (e.g., a smart appliance),other smart devices, a web appliance, a network router, a networkswitch, a network bridge, or any machine capable of executing theinstructions 1116, sequentially or otherwise, that specify actions to betaken by the machine 1100. Further, while only a single machine 1100 isillustrated, the term “machine” shall also be taken to include acollection of machines 1100 that individually or jointly execute theinstructions 1116 to perform any one or more of the methodologiesdiscussed herein.

In various embodiments, the machine 1100 comprises processors 1110,memory 1130, and I/O components 1150, which can be configured tocommunicate with each other via a bus 1102. In an example embodiment,the processors 1110 (e.g., a Central Processing Unit (CPU), a ReducedInstruction Set Computing (RISC) processor, a Complex Instruction SetComputing (CISC) processor, a Graphics Processing Unit (GPU), a DigitalSignal Processor (DSP), an Application Specific Integrated Circuit(ASIC), a Radio-Frequency Integrated Circuit (RFIC), another processor,or any suitable combination thereof) includes, for example, a processor1112 and a processor 1114 that may execute the instructions 1116. Theterm “processor” is intended to include multi-core processors that maycomprise two or more independent processors (also referred to as“cores”) that can execute instructions contemporaneously. Although FIG.11 shows multiple processors, the machine 1100 may include a singleprocessor with a single core, a single processor with multiple cores(e.g., a multi-core processor), multiple processors with a single core,multiple processors with multiples cores, or any combination thereof.

The memory 1130 comprises a main memory 1132, a static memory 1134, anda storage unit 1136 accessible to the processors 1110 via the bus 1102,according to some embodiments. The storage unit 1136 can include amachine-readable medium 1138 on which are stored the instructions 1116embodying any one or more of the methodologies or functions describedherein. The instructions 1116 can also reside, completely or at leastpartially, within the main memory 1132, within the static memory 1134,within at least one of the processors 1110 (e.g., within the processor'scache memory), or any suitable combination thereof, during executionthereof by the machine 1100. Accordingly, in various embodiments, themain memory 1132, the static memory 1134, and the processors 1110 areconsidered machine-readable media 1138.

As used herein, the term “memory” refers to a machine-readable medium1138 able to store data temporarily or permanently and may be taken toinclude, but not be limited to, random-access memory (RAM), read-onlymemory (ROM), buffer memory, flash memory, and cache memory. While themachine-readable medium 1138 is shown in an example embodiment to be asingle medium, the term “machine-readable medium” should be taken toinclude a single medium or multiple media (e.g., a centralized ordistributed database, or associated caches and servers) able to storethe instructions 1116. The term “machine-readable medium” shall also betaken to include any medium, or combination of multiple media, that iscapable of storing instructions (e.g., instructions 1116) for executionby a machine (e.g., machine 1100), such that the instructions, whenexecuted by one or more processors of the machine 1100 (e.g., processors1110), cause the machine 1100 to perform any one or more of themethodologies described herein. Accordingly, a “machine-readable medium”refers to a single storage apparatus or device, as well as “cloud-based”storage systems or storage networks that include multiple storageapparatus or devices. The term “machine-readable medium” shallaccordingly be taken to include, but not be limited to, one or more datarepositories in the form of a solid-state memory (e.g., flash memory),an optical medium, a magnetic medium, other non-volatile memory (e.g.,Erasable Programmable Read-Only Memory (EPROM)), or any suitablecombination thereof. The term “machine-readable medium” specificallyexcludes non-statutory signals per se.

The I/O components 1150 include a wide variety of components to receiveinput, provide output, produce output, transmit information, exchangeinformation, capture measurements, and so on. In general, it will beappreciated that the I/O components 1150 can include many othercomponents that are not shown in FIG. 11. The I/O components 1150 aregrouped according to functionality merely for simplifying the followingdiscussion, and the grouping is in no way limiting. In various exampleembodiments, the I/O components 1150 include output components 1152 andinput components 1154. The output components 1152 include visualcomponents (e.g., a display such as a plasma display panel (PDP), alight emitting diode (LED) display, a liquid crystal display (LCD), aprojector, or a cathode ray tube (CRT)), acoustic components (e.g.,speakers), haptic components (e.g., a vibratory motor), other signalgenerators, and so forth. The input components 1154 include alphanumericinput components (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or other pointinginstruments), tactile input components (e.g., a physical button, a touchscreen that provides location and force of touches or touch gestures, orother tactile input components), audio input components (e.g., amicrophone), and the like.

In some further example embodiments, the I/O components 1150 includebiometric components 1156, motion components 1158, environmentalcomponents 1160, or position components 1162, among a wide array ofother components. For example, the biometric components 1156 includecomponents to detect expressions (e.g., hand expressions, facialexpressions, vocal expressions, body gestures, or eye tracking), measurebiosignals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram basedidentification), and the like. The motion components 1158 includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The environmental components 1160 include, for example, illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometers that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensor components(e.g., machine olfaction detection sensors, gas detection sensors todetect concentrations of hazardous gases for safety or to measurepollutants in the atmosphere), or other components that may provideindications, measurements, or signals corresponding to a surroundingphysical environment. The position components 1162 include locationsensor components (e.g., a Global Positioning System (GPS) receivercomponent), altitude sensor components (e.g., altimeters or barometersthat detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like.

Communication can be implemented using a wide variety of technologies.The I/O components 1150 may include communication components 1164operable to couple the machine 1100 to a network 1180 or devices 1170via a coupling 1182 and a coupling 1172, respectively. For example, thecommunication components 1164 include a network interface component oranother suitable device to interface with the network 1180. In furtherexamples, communication components 1164 include wired communicationcomponents, wireless communication components, cellular communicationcomponents, Near Field Communication (NFC) components, BLUETOOTH®components (e.g., BLUETOOTH® Low Energy), WI-FI® components, and othercommunication components to provide communication via other modalities.The devices 1170 may be another machine or any of a wide variety ofperipheral devices (e.g., a peripheral device coupled via a UniversalSerial Bus (USB)).

Moreover, in some embodiments, the communication components 1164 detectidentifiers or include components operable to detect identifiers. Forexample, the communication components 1164 include Radio FrequencyIdentification (RFID) tag reader components, NFC smart tag detectioncomponents, optical reader components (e.g., an optical sensor to detecta one-dimensional bar codes such as a Universal Product Code (UPC) barcode, multi-dimensional bar codes such as a Quick Response (QR) code,Aztec Code, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code,Uniform Commercial Code Reduced Space Symbology (UCC RSS)-2D bar codes,and other optical codes), acoustic detection components (e.g.,microphones to identify tagged audio signals), or any suitablecombination thereof. In addition, a variety of information can bederived via the communication components 1164, such as location viaInternet Protocol (IP) geo-location, location via WI-FI® signaltriangulation, location via detecting an BLUETOOTH® or NFC beacon signalthat may indicate a particular location, and so forth.

Transmission Medium

In various example embodiments, one or more portions of the network 1180can be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), the Internet, a portion of the Internet, a portion of the PublicSwitched Telephone Network (PSTN), a plain old telephone service (POTS)network, a cellular telephone network, a wireless network, a WI-FI®network, another type of network, or a combination of two or more suchnetworks. For example, the network 1180 or a portion of the network 1180may include a wireless or cellular network, and the coupling 1182 may bea Code Division Multiple Access (CDMA) connection, a Global System forMobile communications (GSM) connection, or another type of cellular orwireless coupling. In this example, the coupling 1182 can implement anyof a variety of types of data transfer technology, such as SingleCarrier Radio Transmission Technology (1xRTT), Evolution-Data Optimized(EVDO) technology, General Packet Radio Service (GPRS) technology,Enhanced Data rates for GSM Evolution (EDGE) technology, thirdGeneration Partnership Project (3GPP) including 3G, fourth generationwireless (4G) networks, Universal Mobile Telecommunications System(UMTS), High Speed Packet Access (HSPA), Worldwide Interoperability forMicrowave Access (WiMAX), Long Term Evolution (LTE) standard, othersdefined by various standard-setting organizations, other long rangeprotocols, or other data transfer technology.

In example embodiments, the instructions 1116 are transmitted orreceived over the network 1180 using a transmission medium via a networkinterface device (e.g., a network interface component included in thecommunication components 1164) and utilizing any one of a number ofwell-known transfer protocols (e.g., Hypertext Transfer Protocol(HTTP)). Similarly, in other example embodiments, the instructions 1116are transmitted or received using a transmission medium via the coupling1172 (e.g., a peer-to-peer coupling) to the devices 1170. The term“transmission medium” shall be taken to include any intangible mediumthat is capable of storing, encoding, or carrying the instructions 1116for execution by the machine 1100, and includes digital or analogcommunications signals or other intangible media to facilitatecommunication of such software.

Furthermore, the machine-readable medium 1138 is non-transitory (inother words, not having any transitory signals) in that it does notembody a propagating signal. However, labeling the machine-readablemedium 1138 “non-transitory” should not be construed to mean that themedium is incapable of movement; the medium should be considered asbeing transportable from one physical location to another. Additionally,since the machine-readable medium 1138 is tangible, the medium may beconsidered to be a machine-readable device.

Language

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Although an overview of the inventive subject matter has been describedwith reference to specific example embodiments, various modificationsand changes may be made to these embodiments without departing from thebroader scope of embodiments of the present disclosure. Such embodimentsof the inventive subject matter may be referred to herein, individuallyor collectively, by the term “invention” merely for convenience andwithout intending to voluntarily limit the scope of this application toany single disclosure or inventive concept if more than one is, in fact,disclosed.

The embodiments illustrated herein are described in sufficient detail toenable those skilled in the art to practice the teachings disclosed.Other embodiments may be used and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. The Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

As used herein, the term “or” may be construed in either an inclusive orexclusive sense. Moreover, plural instances may be provided forresources, operations, or structures described herein as a singleinstance. Additionally, boundaries between various resources,operations, modules, engines, and data stores are somewhat arbitrary,and particular operations are illustrated in a context of specificillustrative configurations. Other allocations of functionality areenvisioned and may fall within a scope of various embodiments of thepresent disclosure. In general, structures and functionality presentedas separate resources in the example configurations may be implementedas a combined structure or resource. Similarly, structures andfunctionality presented as a single resource may be implemented asseparate resources. These and other variations, modifications,additions, and improvements fall within a scope of embodiments of thepresent disclosure as represented by the appended claims. Thespecification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense.

What is claimed is:
 1. A system comprising: a data module to accesshistorical data that comprises advertisement revenue, advertisementparameters, and a cannibalization metric that is indicative of salesloss associated with an advertisement presentation; an analysis module,implemented by at least one hardware processor of a machine, todetermine a value for at least one of the advertisement parameters by ananalysis of the historical data, use of the determined value causes adesired advertisement revenue with respect to a bounded cannibalizationmetric; and a presentation module to cause presentation, in real time,of an advertisement on a user interface of a client device using thedetermined value.
 2. The system of claim 1, wherein the analysis moduleis further to: identify a cannibalization covariate from among candidatecovariates that include the advertisement parameters; generate acovariate model that models the cannibalization covariate with respectto the advertisement parameters; generate a cannibalization model thatmodels the cannibalization metric with respect to the cannibalizationcovariate; generate a revenue model that models the advertisementrevenue with respect to the advertisement parameters; and determine thevalue for at least one of the advertisement parameters using the revenuemodel in conjunction with the cannibalization model and the covariatemodel.
 3. The system of claim 1, wherein the cannibalization metriccomprises at least one of a purchase per user per week metric (PPW) anda gross revenue per user per week metric (GPW).
 4. The system of claim1, wherein the advertisement parameters include at least one of anadvertisement placement, impressions, clicks, an advertiser, and anadvertisement type.
 5. A method comprising: accessing historical datacomprising advertisement revenue, advertisement parameters, and acannibalization metric that is indicative of sales loss associated withan advertisement presentation; determining a value for at least one ofthe advertisement parameters by analyzing the historical data, use ofthe determined value causes a desired advertisement revenue with respectto a bounded cannibalization metric; and causing presentation, in realtime, of an advertisement on a user interface of a client device usingthe determined value.
 6. The method of claim 5, wherein the analyzingthe historical data further comprises: identifying a cannibalizationcovariate from among candidate covariates that include the advertisementparameters; generating a covariate model that models the cannibalizationcovariate with respect to the advertisement parameters; generating acannibalization model that models the cannibalization metric withrespect to the cannibalization covariate; generating a revenue modelthat models the advertisement revenue with respect to the advertisementparameters; and determining the value for at least one of theadvertisement parameters using the revenue model in conjunction with thecannibalization model and the covariate model.
 7. The method of claim 6,wherein the identifying the cannibalization covariate further comprises:measuring a cannibalization value by comparing cannibalization of acontrol group of users shown advertisements and a treatment group ofusers not shown advertisements; and identifying the cannibalizationcovariate from among the candidate covariates according to a correlationbetween respective candidate covariates and the measured cannibalizationvalue, the cannibalization covariate being a highest correlatedcovariate among the candidate covariates.
 8. The method of claim 7,wherein the candidate covariates include at least one of clicks,impressions, and page views.
 9. The method of claim 5, wherein thecannibalization metric comprises at least one of a purchase per user perweek metric (PPW) and a gross revenue per user per week metric (GPW).10. The method of claim 5, wherein the advertisement parameters includeat least one of an advertisement placement, impressions, clicks, anadvertiser, and an advertisement type.
 11. The method of claim 5,further comprising: determining a lower limit for the boundedcannibalization metric according to a minimum advertisement revenuespecified by an operator; and determining an upper limit for the boundedcannibalization metric according to a maximum cannibalization costspecified by the operator.
 12. The method of claim 5, furthercomprising: accessing current data comprising the advertisement revenue,the advertisement parameters, and the cannibalization metric for a timeperiod of a duration; determining a change amount for at least one ofthe advertisement parameters by analyzing the historical data inconjunction with the current data; and causing presentation, in realtime, of the advertisement on the user interface of the client deviceusing the determined change amount for another time period of theduration.
 13. A machine-readable medium having no transitory signals andstoring instructions that, when executed by at least one processor of amachine, cause the machine to perform operations comprising: accessinghistorical data comprising advertisement revenue, advertisementparameters, and a cannibalization metric that is indicative of salesloss associated with an advertisement presentation; determining a valuefor at least one of the advertisement parameters by analyzing thehistorical data, use of the determined value causes a desiredadvertisement revenue with respect to a bounded cannibalization metric;and causing presentation, in real time, of an advertisement on a userinterface of a client device using the determined value.
 14. Themachine-readable medium of claim 13, wherein the analyzing thehistorical data further comprises: identifying a cannibalizationcovariate from among the candidate covariates that include theadvertisement parameters; generating a covariate model that models thecannibalization covariate with respect to the advertisement parameters;generating a cannibalization model that models the cannibalizationmetric with respect to the cannibalization covariate; generating arevenue model that models the advertisement revenue with respect to theadvertisement parameters; and determining the value for at least one ofthe advertisement parameters using the revenue model in conjunction withthe cannibalization model and the covariate model.
 15. Themachine-readable medium of claim 14, wherein the operations furthercomprise the identifying the cannibalization covariate by: measuring acannibalization value by comparing cannibalization of a control group ofusers shown advertisements and a treatment group of users not shownadvertisements; and identifying the cannibalization covariate from amongthe candidate covariates according to a correlation between respectivecandidate covariates and the measured cannibalization value, thecannibalization covariate being a highest correlated covariate among thecandidate covariates.
 16. The machine-readable medium of claim 15,wherein the candidate covariates include at least one of clicks,impressions, and page views.
 17. The machine-readable medium of claim13, wherein the cannibalization metric comprises at least one of apurchase per user per week metric (PPW) and a gross revenue per user perweek metric (GPW).
 18. The machine-readable medium of claim 13, whereinthe advertisement parameters include at least one of an advertisementplacement, impressions, clicks, an advertiser, and an advertisementtype.
 19. The machine-readable medium of claim 13, wherein theoperations further comprise: determining a lower limit for the boundedcannibalization metric according to a minimum advertisement revenuespecified by an operator; and determining an upper limit for the boundedcannibalization metric according to a maximum cannibalization costspecified by the operator.
 20. The machine-readable medium of claim 13,further comprising: accessing current data comprising the advertisementrevenue, the advertisement parameters, and the cannibalization metricfor a time period of a duration; determining a change amount for atleast one of the advertisement parameters by analyzing the historicaldata in conjunction with the current data; and causing presentation, inreal time, of the advertisement on the user interface of the clientdevice using the determined change amount for another time period of theduration.